Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain.For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks.Contemporary large language models (LLMs), however, make it possible to interrogate the latent structure of conceptual representations using experimental methods nearly identical to those commonly used with human participants.The current work utilizes three common techniques borrowed from cognitive psychology to estimate and compare the structure of concepts in humans and a suite of LLMs.In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation.Structures estimated from LLM behavior, while individually fairly consistent with those estimated from human behavior, vary much more depending upon the particular task used to generate responsesacross tasks, estimates of conceptual structure from the very same model cohere less with one another than do human structure estimates.These results highlight an important difference between contemporary LLMs and human cognition, with implications for understanding some fundamental limitations of contemporary machine language.

In this paper, a new method for image texture representation is proposed, which represents image content using a 49 dimensional feature vector through calculating the variation of texture direction and the intensity of texture. In addition, the texture feature is grouped into a feature set with some other image texture representation methods, and then a new online feature selection method with a novel discrimination criterion is presented. We test the discriminating ability of every feature in the feature set utilizing the discrimination criterion, and select the optimal feature subset, which expresses image content in an even better fashion. The results of the computer simulation experiments show that the proposed feature extraction and feature selection method can represent image content effectively, and improve the retrieval precision visibly.